What is a Supply Chain Digital Twin?

Modeling and simulation are common practices, used by organizations every day for decades. Knowing how certain factors or inputs will affect your operations is vital to forecasting and strategic planning.

But a lot has changed. Vast data collection along with inputs from Internet of Things (IoT) sensors allow real-time data to feed into accurate and verifiable Supply Chain Digital Twins. Whether a global pandemic hinders your suppliers, a hurricane closes production and warehousing facilities, or an organization simply wants to operate a better way, the ability to run what-if analysis and immediately create an execution plan has never been more important.

A Supply Chain Digital Twin is a complete digital replica that mirrors a real-world system’s assets, transactions, third-party relationships, operational processes, and the entire supply chain network in real-time to predict its dynamics and optimize action plans. It identifies where volatility and uncertainty exist and enables scenario planning to allow an organization to make decisions based on the needs of the business, instead of resolving problems when and as they arise.

Criteria of a Supply Chain Digital Twin:

  • Necessary details to thoroughly evaluate supply chain interactions, from large-scale shifts in demand to minor aspects at one single location. The Digital Twin should assist with identifying demand variability, flow of financials or individual SKUs, and general what-if analysis.
  • Real-time data feeds, including traffic schedules, fleet location, and inventory levels to correctly analyze the supply chain’s current status and develop updated forecasts. Is your company facing the bullwhip effect from inefficient inventory policies?
  • Customizable alerts or notifications to bring visibility to irregular situations such as service levels dropping below targeted thresholds.
  • Configurable triggers that will automatically take place when selected events occur, such as potential stock outs.
  • Allow you to develop action plans to help you address abnormal situations, and test those plans to ensure they are effective.
  • Integrate with complimentary IT databases and business intelligence tools—such as a supply chain “control tower.”

Consumer Packaged Goods Example: Tetra Pak

CPG presents a unique set of challenges and opportunities for Digital Twin technologies. Rather than involving few large and complex assets, retail markets often involve millions of much simpler objects. Firms participating in this sector have their focus on tracking the flow of products through supply chains and building systems that can extract valuable insights through aggregated data to create incremental changes that have large scale impact.

Tetra Pak is the world’s leading food processing and packaging solutions company, founded in 1943 and currently serving more than 160 countries. Southeast Asia is home to one of Tetra Pak’s largest warehouses, and this location served as the first smart warehouse to incorporate digital twin technology to better understand and manage physical assets.

By combining the IoT technology with data analytics, Tetra Pak was able to create a unique virtual representation of its physical warehouse that monitors and simulates the behavior of the warehouse assets in real-time. With a digital twin solution, Tetra Pak can maintain 24 / 7 coordination of its operations to resolve issues as they occur. Warehouse managers can use real-time operational data to make intelligent decisions to reduce congestion, improve resource planning and allocate labor. A control tower also monitors the flow of both inbound and outbound goods to maintain efficiency, enabling Tetra Pak to shelve inbound goods correctly within 30 minutes of receipt and all outbound products are ready for shipment within 95 minutes.

Logistics Example: The Singapore Port Authority

The Logistics industry has not yet achieved widespread adoption of Digital Twins, but many of the core technologies are already in place. Today, open API strategies are being embraced while sensors to track shipments and material handling equipment are increasingly being utilized. Companies are applying machine learning and advanced analytics techniques to optimize the supply chains and better understand future scenarios from historical shipment and operational data.

The Singapore Port Authority, along with the National University in Singapore, is currently developing a Digital Twin of the country’s new mega-hub for container shipping. The flow of goods depends on multiple factors working in concert with one another, including ships, trucks, aircraft, information systems, and most importantly, people. These multi-stakeholder environments can be seen most clearly at major logistics hubs such as international ports where the challenge of operating efficiently can be exacerbated by depending on so many participants and resources.

The hope is that a Digital Twin will help optimize management of this facility and reduce congestion. Simulation will assist with choosing the optimal docking location for a vessel of any size, considering the assets, space, and personnel required for loading or unloading operations, and how these resources are shared between multiple vessels at any given time. 

Professor Lee Hoo Hay from the National University of Singapore states, “Simulation-based optimization, industry 4.0, and the internet of things have been around for some time now. However it has really been the boom of artificial intelligence and its predictive capabilities that have given digital twins a big push in creating new value. In the past, creating spatial models digitally was exciting, but failed to be more than a way to visualize an object statically. Today, all the data we have from sensors, historical performance, and inputs about behavior lends itself to being linked to the spatial model and to predicting future behavior by changing different inputs. Effectively the data and prediction capabilities make the spatial model come alive.”

Manufacturing Example: CNH Industrial 

Manufacturing operations are a primary focus for Digital Twin development, primarily due to factories having an abundance of data on producing core physical assets. Utilizing automation and machine learning on production lines can drive tremendous value, as modest improvements to throughput, quality control, or equipment reliability can result in millions of dollars saved.

CNH Industrial is a global producer of industrial, commercial, and agricultural vehicles. The company has incorporated a Digital Twin to optimize maintenance of its plant in Suzzara, Italy, and improve the reliability of robotic welding machines on the plant’s chassis line. CNH’s welding robots depend on a flexible copper conductor to deliver electrical current to their welding heads. These parts have a finite life and accumulated wear can cause the conductor to melt, disrupting production and damaging the robot itself.

A model was built to include the different types of chassis and their welding requirements, the automatic welding stations staged along the production line, and the individual robots in each station. Data is fed into the model by the plant’s planning systems and by the condition monitoring system outfitted on each robot. By using simulation and machine learning, the Digital Twin is able to run what-if analysis on different probability of component failure and the impact of different operational and maintenance strategies to optimize downtime and spare part usage. 

In summary, Digital Twin technologies have the capability to transform almost every industry. As these models enter widespread use, their impact will be seen at every stage of the supply chain. Manufacturing will be faster and more agile. Data on product performance will usher a more proactive approach that allows companies to offer their customers better service and intervene earlier to prevent failures or complications.

Get in touch with Velis today so we can help your firm engineer a better way





Moshood, T.D.; Nawanir, G.; Sorooshian, S.; Okfalisa, O. Digital Twins Driven Supply Chain Visibility within Logistics: A New Paradigm for Future Logistics. Appl. Syst. Innov. 2021, 4, 29. https://doi.org/10.3390/asi4020029


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